Do fundamental fears differentially contribute to pain‐related fear and pain catastrophizing? An evaluation of the sensitivity index
Bibliographic record
Abstract
Three fundamental fears - anxiety sensitivity (AS), injury/illness sensitivity (IS) and fear of negative evaluation (FNE) - have been proposed to underlie common fears and psychopathological conditions. In pain research, the relation between AS and (chronic) pain processes was the subject of several studies, whereas the possible role of IS has been ignored. The current research examines the role of IS with respect to various pain-related variables in two studies. In the first study, 192 healthy college students completed the Sensitivity Index (SI; a composite measure assessing the three fundamental fears) and various pain-related questionnaires. In a second study, 60 students out of the original sample took part in a pain induction procedure and completed the SI as well. We first examined the properties of the SI. Factor analysis on the SI replicated the proposed factor structure [Taylor S. The structure of fundamental fears, J Behav Ther Exp Psychiat 1993;24:289-99]. However, some items of the ASI did show problematic loadings and were therefore excluded in subsequent analyses. The main hypothesis of the current study states that IS is a stronger predictor than AS of pain catastrophizing and fear of pain as assessed by self-report measures, and of pain tolerance and anticipatory fear of pain as assessed in a pain induction study. This hypothesis could be confirmed for all variables, except for pain tolerance, which was not predicted by any of the three fundamental fears. The current study can be considered as an impetus for devoting attention to IS in future pain research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".